NASA Technical Reports Server (NTRS) 20120009229: Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH

Software

Op Automated Performance
Characterization of DSN
System Frequency Stability
Using Spacecraft Tracking
Data

This software provides an automated
capability to measure and qualify the fre-
quency stability performance of the Deep
Space Network (DSN) ground system,
using daily spacecraft tracking data. The
results help to verify if die DSN perform-
ance is meeting its specification, therefore
ensuring commitments to flight missions;
in particular, the radio science investiga-
tions. The rich set of data also helps the
DSN Operations and Maintenance team
to identify the trends and patterns, allow-
ing them to identify the antennas of lower
performance and implement corrective
action in a timely manner.

Unlike the traditional approach
where the performance can only be ob-
tained from special calibration sessions
that are both time-consuming and re-
quire manual setup, the new method
taps into the daily spacecraft tracking
data. This new approach significantly in-
creases the amount of data available for
analysis, roughly by two orders of mag-
nitude, making it possible to conduct
trend analysis with good confidence.

The software is built with automation
in mind for end-to-end processing. From
the inputs gathering to computation
analysis and later data visualization of
the results, all steps are done automati-
cally, making the data production at
near zero cost. This allows the limited
engineering resource to focus on high-
level assessment and to follow up with
the exceptions/deviations.

To make it possible to process the con-
tinual stream of daily incoming data
without much effort, and to understand
the results quickly, the processing needs
to be automated and the data summa-
rized at a high level. Special attention
needs to be given to data gathering,
input validation, handling anomalous
conditions, computation, and presenting
the results in a visual form that makes it
easy to spot items of exception/ deviation
so that further analysis can be directed
and corrective actions followed.

This work was done by Timothy T. Pham,
Richard J. Machuzak, Alina Bedrossian,
Richard M. Kelly, and Jason C. Liao of Cal-
tech for NASA’s Jet Propulsion Laboratory.

For more information, contact iaoffice
@jpl.nasa.gov.

This software is available for commercial li-
censing. Please contact Daniel Broderick of
the California Institute of Technology at
danielb@caltech.edu. Refer to NPO-47532.

@ Histogrammatic Method
for Determining Relative
Abundance of Input Gas
Pulse

To satisfy the Major Constituents Analy-
sis (MCA) requirements for the Vehicle
Cabin Atmosphere Monitor (VCAM) , this
software analyzes the relative abundance
ratios for N 9 , CD, Ar, and CO 2 as a func-
tion of time and constructs their best-esti-
mate mean. A histogram is first built of all
abundance ratios for each of the species
vs time. The abundance peaks correspon-
ding to the intended measurement and
any obfuscating background are then sep-
arated via standard peak-finding tech-
niques in histogram space. A voting
scheme is then used to include/exclude
this particular time sample in the final av-
erage based on its membership to the in-
tended measurement or the background
population. This results in a robust and
reasonable estimate of the abundance of
trace components such as CO 2 and Ar
even in the presence of obfuscating back-
grounds internal to the VCAM device.

VCAM can provide a means for moni-
toring the air within the enclosed envi-
ronments, such as the ISS (International
Space Station), Crew Exploration Vehi-
cle (CEV), a Lunar Habitat, or another
vehicle traveling to Mars. Its miniature
pre-concentrator, gas chromatograph
(GC), and mass spectrometer can pro-
vide unbiased detection of a large num-
ber of organic species as well as MCA
analysis. VCAM’s software can identify
the concentration of trace chemicals
and whether the chemicals are on a tar-
geted list of hazardous compounds. This
innovation’s performance and reliability
on orbit, along with the ground team’s
assessment of its raw data and analysis re-
sults, will validate its technology for fu-
ture use and development.

This work was done by Lukas Mandrake,
Benjainin J. Bomstein, Stojan Madzunkov,
and John A. MacAskill of Caltech for NASA’s
Jet Propulsion Laboratory.

This software is available for commercial li-
censing. Please contact Daniel Broderick of

the California Institute of Technology at
danielb@caltech.edu. Refer to NPO-4721 7.

Predictive Sea State Estima-
tion for Automated Ride
Control and Handling —
PSSEARCH

PSSEARCH provides predictive sea
state estimation, coupled with closed-
loop feedback control for automated
ride control. It enables a manned or un-
manned watercraft to determine the 3D
map and sea state conditions in its vicin-
ity in real time. Adaptive path-plan-
ning/ replanning software and a control
surface management system will then
use this information to choose the best
settings and heading relative to the seas
for the watercraft.

PSSEARCH looks ahead and antici-
pates potential impact of waves on the
boat and is used in a tight control loop to
adjust trim tabs, course, and throttle set-
tings. The software uses sensory inputs
including IMU (Inertial Measurement
Unit), stereo, radar, etc. to determine
the sea state and wave conditions (wave
height, frequency, wave direction) in the
vicinity of a rapidly moving boat. This in-
formation can then be used to plot a
“safe” path through the oncoming waves.

The main issues in determining a safe
path for sea surface navigation are: (1)
deriving a 3D map of the surrounding
environment, (2) extracting hazards
and sea state surface state from the im-
aging sensors/map, and (3) planning a
path and control surface settings that
avoid the hazards, accomplish the mis-
sion navigation goals, and mitigate crew
injuries from excessive heave, pitch, and
roll accelerations while taking into ac-
count the dynamics of the sea surface
state. The first part is solved using a wide
baseline stereo system, where 3D struc-
ture is determined from two calibrated
pairs of visual imagers.

Once the 3D map is derived, anything
above the sea surface is classified as a po-
tential hazard and a surface analysis
gives a static snapshot of the waves. Dy-
namics of the wave features are obtained
from a frequency analysis of motion vec-
tors derived from the orientation of the
waves during a sequence of inputs. Fu-
sion of the dynamic wave patterns with
the 3D maps and the IMU outputs is
used for efficient safe path planning.

NASA Tech Briefs, May 2012

15

This work was done by Terrance L. Hunts-
berger, Andrew B. Howard, Hrand Aghazar-
ian, and Arturo L. Rankin of Caltech for
NASA’s Jet Propulsion Laboratory. Further in-
formation is contained in a TSP ( see page 1 ).

In accordance with Public Law 96-51 7,
the contractor has elected to retain title to this
invention. Inquiries concerning rights for its
commercial use should be addressed to:
Innovative Technology Assets Management

JPL

Mail Stop 202-233
4800 Oak Grove Drive
Pasadena, CA 91109-8099
E-mail: iaoffice@jpl.nasa.gov
Refer to NPO-4 7533, volume and number
of this NASA Tech Briefs issue, and the
page number.

Qjl LEGION: Lightweight Ex-
pandable Group of Inde-
pendently Operating Nodes

LEGION is a lightweight C-language
software library that enables distrib-
uted asynchronous data processing
with a loosely coupled set of compute
nodes. Loosely coupled means that a
node can offer itself in service to a
larger task at any time and can with-
draw itself from service at any time,
provided it is not actively engaged in
an assignment. The main program, i.e.,
the one attempting to solve the larger
task, does not need to know up front
which nodes will be available, how
many nodes will be available, or at what
times the nodes will be available, which
is normally the case in a “volunteer
computing” framework. The LEGION
software accomplishes its goals by pro-
viding message-based, inter-process
communication similar to MPI (mes-
sage passing interface), but without the
tight coupling requirements. The soft-
ware is lightweight and easy to install as
it is written in standard C with no ex-
otic library dependencies.

LEGION has been demonstrated in a
challenging planetary science applica-
tion in which a machine learning system
is used in closed-loop fashion to effi-
ciently explore the input parameter
space of a complex numerical simula-
tion. The machine learning system de-
cides which jobs to run through the sim-
ulator; then, through LEGION calls, the
system farms those jobs out to a collec-
tion of compute nodes, retrieves the job
results as they become available, and up-
dates a predictive model of how the sim-
ulator maps inputs to outputs. The ma-
chine learning system decides which
new set of jobs would be most informa-

tive to run given the results so far; this
basic loop is repeated until sufficient in-
sight into the physical system modeled
by the simulator is obtained.

This work was done by Michael C. Burl of
Caltech for NASA’s Jet Propulsion Labora-
tory. Further information is contained in a
TSP (see page 1).

This software is available for commercial li-
censing. Please contact Daniel Broderick of
the California Institute of Technology at
danielb@caltech.edu. Refer to NPO-47910.

Real-Time Projection to
Verify Plan Success
During Execution

The Mission Data System provides a
framework for modeling complex sys-
tems in terms of system behaviors and
goals that express intent. Complex activ-
ity plans can be represented as goal net-
works that express the coordination of
goals on different state variables of the
system. Real-time projection extends the
ability of this system to verify plan achiev-
ability (all goals can be satisfied over the
entire plan) into the execution domain
so that the system is able to continuously
re-verify a plan as it is executed, and as
the states of the system change in re-
sponse to goals and the environment.

Previous versions were able to detect
and respond to goal violations when
they actually occur during execution.
This new capability enables the predic-
tion of future goal failures; specifically,
goals that were previously found to be
achievable but are no longer achievable
due to unanticipated faults or environ-
mental conditions. Early detection of
such situations enables operators or an
autonomous fault response capability to
deal with the problem at a point that
maximizes the available options.

For example, this system has been ap-
plied to the problem of managing bat-
tery energy on a lunar rover as it is used
to explore the Moon. Astronauts drive
the rover to waypoints and conduct sci-
ence observations according to a plan
that is scheduled and verified to be
achievable with the energy resources
available. As the astronauts execute this
plan, the system uses this new capability
to continuously re-verify the plan as en-
ergy is consumed to ensure that the bat-
tery will never be depleted below safe
levels across the entire plan.

In particular, this enables an execu-
tion system to predict problems such as
resource exhaustion before they occur.
The models are expressed and executed
in a way that can be optimized for real-

time use in an embedded system.

This work was done by David A. Wagner,
Daniel L. Dvorak, Robert D. Rasmussen, Rus-
sell L. Knight, John R. Morris, Matthew B.
Bennett, and Michel D. Ingham of Caltech for
NASA’s Jet Propulsion Laboratory. For more
information, contact iaofjiceMjpl. nasa.gov.

This software is available for commercial li-
censing. Please contact Daniel Broderick of the
California Institute of Technology at
danielb@caltech.edu. Refer to NPO-47734.

Automated Performance
Characterization of DSN
System Frequency Stability
Using Spacecraft Tracking
Data

This software provides an automated
capability to measure and qualify the fre-
quency stability performance of the Deep
Space Network (DSN) ground system,
using daily spacecraft tracking data. The
results help to verify if the DSN perform-
ance is meeting its specification, therefore
ensuring commitments to flight missions;
in particular, the radio science investiga-
tions. The rich set of data also helps the
DSN Operations and Maintenance team
to identify the trends and patterns, allow-
ing them to identify the antennas of lower
performance and implement corrective
action in a timely manner.

Unlike the traditional approach
where the performance can only be ob-
tained from special calibration sessions
that are both time-consuming and re-
quire manual setup, the new method
taps into the daily spacecraft tracking
data. This new approach significantly
increases the amount of data available
for analysis, roughly by two orders of
magnitude, making it possible to con-
duct trend analysis with good confi-
dence.

The software is built with automation
in mind for end-to-end processing.
From the inputs gathering to computa-
tion analysis and later data visualization
of the results, all steps are done auto-
matically, making the data production at
near zero cost. This allows the limited
engineering resource to focus on high-
level assessment and to follow up with
the exceptions/ deviations.

To make it possible to process the con-
tinual stream of daily incoming data
without much effort, and to understand
the results quickly, the processing needs
to be automated and the data summa-
rized at a high level. Special attention
needs to be given to data gathering,
input validation, handling anomalous
conditions, computation, and present-

16

NASA Tech Briefs, May 2012